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Multiple Criteria Group Decision-Making Based on Hesitant Fuzzy Linguistic Consensus Model for Fashion Sales Forecasting

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Book cover Artificial Intelligence on Fashion and Textiles (AITA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 849))

Abstract

In many real-world multiple criteria group decision-making process, people cannot provide accurate preference information over a set of alternatives because of the increasingly complex environment. Fashion sales forecasting can be taken as a multi-criteria group decision-making problem given that people need to consider product life cycle, year-on-year growth rate, seasonal factor, industry factor, and consumer factor comprehensively when they forecast the fashion scales. In this paper, we developed a fuzzy linguistic model for fashion sales forecasting. Approaches such as hesitant fuzzy linguistic preference relation and ordinal consensus measure are used in our paper. Decision-makers compare alternatives over each criterion and the ranking of alternatives can be derived. Based on the ranking provided by the decision-makers, we introduce the ordinal consensus of the group. Then, a consensus reaching process is given to raise the degree of consensus.

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References

  1. Kuo, R.J., Xue, K.C.: A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights. Decis. Support Syst. 24, 105–126 (1998). https://doi.org/10.1016/S0167-9236(98)00067-0

    Article  Google Scholar 

  2. Choi, T.M., Chow, P.S.: Mean-variance analysis of quick response program. Int. J. Prod. Econ. 114, 456–475 (2008). https://doi.org/10.1016/j.ijpe.2007.06.009

    Article  Google Scholar 

  3. Liu, W.S., Liao, H.C.: A bibliometric analysis of fuzzy decision research during 1970–2015. Int. J. Fuzzy Syst. 19, 1–14 (2017). https://doi.org/10.1007/s40815-016-0272-z

    Article  Google Scholar 

  4. Adnan, M.R.H.M., Sarkheyli, A., Zain, A.M., Haron, H.: Fuzzy logic for modeling machining process: a review. Artif. Intelli. Rev. 43, 345–379 (2015). https://doi.org/10.1007/s10462-012-9381-8

    Article  Google Scholar 

  5. Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20, 109–119 (2012). https://doi.org/10.1109/TFUZZ.2011.2170076

    Article  Google Scholar 

  6. Liao, H.C., Xu, Z.S., Zeng, X.J.: Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Trans. Fuzzy Syst. 23, 1343–1355 (2015). https://doi.org/10.1109/TFUZZ.2014.2360556

    Article  Google Scholar 

  7. Liao, H.C., Yang, L.Y., Xu, Z.S.: Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets. Appl. Soft Comput. 63, 223–234 (2018). https://doi.org/10.1016/j.asoc.2017.11.049

    Article  Google Scholar 

  8. Liao, H.C., Xu, Z.S., Enrique, H.V., Herrera, F.: Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the art survey. Int. J. Fuzzy Syst. (2018). https://doi.org/10.1007/s40815-017-0432-9

    Article  MathSciNet  Google Scholar 

  9. Rodríguez, R.M., Martínez, L., Herrera, F.: A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Inform. Sci. 241, 28–42 (2013). https://doi.org/10.1016/j.ins.2013.04.006

    Article  MathSciNet  MATH  Google Scholar 

  10. Liao, H.C., Xu, Z.S., Zeng, X.J., Merigó, J.M.: Framework of group decision making with intuitionistic fuzzy preference information. IEEE Trans. Fuzzy Syst. 23, 1211–1227 (2015). https://doi.org/10.1109/TFUZZ.2014.2348013

    Article  Google Scholar 

  11. Wu, Z.B., Xu, J.P.: Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega 65, 28–40 (2016). https://doi.org/10.1016/j.omega.2015.12.005

    Article  Google Scholar 

  12. Wu, Z.B., Xu, J.P.: An interactive consensus reaching model for decision making under hesitation linguistic environment. J. Intell. Fuzzy Syst. 31, 1635–1644 (2016). https://doi.org/10.3233/JIFS-151708

    Article  MATH  Google Scholar 

  13. Liao, H.C., Xu, Z.S., Zeng, X.J., Merigó, J.M.: Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowle. Based Syst. 76, 127–138 (2015). https://doi.org/10.1016/j.knosys.2014.12.009

    Article  Google Scholar 

  14. Zhu, B., Xu, Z.S.: Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Trans. Fuzzy Syst. 22, 35–45 (2014). https://doi.org/10.1109/TFUZZ.2013.2245136

    Article  Google Scholar 

  15. Saint, S., Lawson, J.R.: Rules for reaching consensus: a modern approach to decision making. Jossey-Bass, San Francisco (1994)

    Google Scholar 

  16. Xu, Y.J., Wang, H.M.: A group consensus decision support model for hesitant 2-tuple fuzzy linguistic preference relations with additive consistency. J. Intell. Fuzzy Syst. 33, 41–54 (2017). https://doi.org/10.3233/JIFS-161029

    Article  MATH  Google Scholar 

  17. Cook, W.D.: Distance-based and ad hoc consensus models in ordinal preference ranking. Eur. J. Oper. Res. 172, 369–385 (2006). https://doi.org/10.1016/j.ejor.2005.03.048

    Article  MathSciNet  MATH  Google Scholar 

  18. Xia, M., Zhang, Y.C., Weng, L.G., Ye, X.L.: Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowle. Based Syst. 36, 253–259 (2012). https://doi.org/10.1016/j.knosys.2012.07.002

    Article  Google Scholar 

  19. Chang, P.C., Wang, Y.W.: Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert Syst. Appl. 30, 715–726 (2006). https://doi.org/10.1016/j.eswa.2005.07.031

    Article  Google Scholar 

  20. Sun, Z.L., Choi, T.M., Au, K.F., Yu, Y.: Sales forecasting using extreme learning machine with applications in fashion retailing. Decis. Support Syst. 46, 411–419 (2008). https://doi.org/10.1016/j.dss.2008.07.009

    Article  Google Scholar 

  21. Lin, C.T., Lee, I.F.: Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system. Expert Syst. Appl. 36, 8385–8390 (2009). https://doi.org/10.1016/j.eswa.2008.10.057

    Article  Google Scholar 

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (71501135, 71771156), the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23), the 2018 Key Project of the Key Research Institute of Humanities and Social Sciences in Sichuan Province (No. LYC18-02, No. DSWL18-2), and the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23).

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610064, China

    Ming Tang & Huchang Liao

Authors
  1. Ming Tang
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  2. Huchang Liao
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Correspondence to Huchang Liao .

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Editors and Affiliations

  1. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hunghom, Hong Kong

    Wai Keung Wong

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Tang, M., Liao, H. (2019). Multiple Criteria Group Decision-Making Based on Hesitant Fuzzy Linguistic Consensus Model for Fashion Sales Forecasting. In: Wong, W. (eds) Artificial Intelligence on Fashion and Textiles. AITA 2018. Advances in Intelligent Systems and Computing, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-319-99695-0_40

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